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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2022 / Cilt: 24 Sayı: 72

A Data Mining Application in Customer Churn Prediction

Müşteri Kayıplarının Tahmini Üzerine Bir Veri Madenciliği Uygulaması

Mustafa Büyükkeçeci0000-0002-1970-8952
Mehmet Cudi Okur(YASAR UNIVERSITY)0000-0002-0096-9087
Pages
887–900
Publication Date
2022-09-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, it is aimed to obtain feature subsets with optimal number of elements and to compare model performances before and after feature selection.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Customer satisfaction and loyalty can be achieved through reasonable prices, product variety, fast supply and delivery, product quality, pre and post-sales services and analysis of customer behaviors.
📊
Key Findings & Data:To this end, experiments were carried out using a total of 21 classification methods and datasets from the telecommunications, banking, and e-commerce industries.
💡
Conclusion & Impact:Thus, it is aimed to obtain feature subsets with optimal number of elements and to compare model performances before and after feature selection.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Veri Tabanlarında Bilgi Keşfi (VTBK) · Veri Madenciliği · RFM Bölümlemesi

Abstract

Customer satisfaction and loyalty can be achieved through reasonable prices, product variety, fast supply and delivery, product quality, pre and post-sales services and analysis of customer behaviors. Businesses that analyze customer behavior can both retain existing customers and gain new ones. This study aims to build supervised models that can predict customers who are likely to leave businesses. To this end, experiments were carried out using a total of 21 classification methods and datasets from the telecommunications, banking, and e-commerce industries. In addition, RFM (Recency, Frequency, Monetary Value) segmentation, a simple but effective marketing analysis tool used by businesses to rank and classify customers according to their spending habits, was used as a dimension reduction method together with the Chi-Square Test. Thus, it is aimed to obtain feature subsets with optimal number of elements and to compare model performances before and after feature selection.

Özet

Müşteri memnuniyeti ve sadakati uygun fiyat, ürün çeşitliliği, hızlı tedarik ve sevkiyat, ürün kalitesi, satış öncesi ve sonrası hizmetler ve müşteri davranışlarının analiz edilmesi ile sağlanır. Müşteri davranışlarını analiz eden işletmeler hem mevcut müşterilerini koruyabilir hem de yenilerini kazanabilir. Bu çalışmanın amacı işletmeleri terk etme ihtimali olan müşterileri tahmin edebilen gözetimli modeller üretmektir. Bu amaçla toplamda 21 sınıflandırma yöntemi ve telekomünikasyon, bankacılık ve e–ticaret sektörlerine ait veri kümeleri kullanılarak deney çalışmaları gerçekleştirilmiştir. Ayrıca işletmelerin harcama alışkanlıklarına göre müşterileri sıralamak ve sınıflandırmak için kullandıkları basit ama etkili bir pazarlama analiz aracı olan RFM (Recency, Frequency, Monetary Value) bölümlemesi, Ki-Kare Testi ile birlikte boyut indirgeme metodu olarak kullanılmıştır. Böylelikle optimal eleman sayısına sahip öznitelik altkümelerinin elde edilmesi ve öznitelik seçim öncesi ve sonrası model performanslarının kıyaslanması hedeflenmiştir.

🔖

Cite This Article

Büyükkeçeci, M., Okur, M. C. (2022). A Data Mining Application in Customer Churn Prediction. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 24 Sayı: 72, 887–900. https://doi.org/10.21205/deufmd.2022247218

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